{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Bauckneht M"],"funding":["Associazione Italiana per la Ricerca sul Cancro","Ministero della Salute"],"pagination":["3"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9811737"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["21(1)"],"pubmed_abstract":["<h4>Background</h4>Positron Emission Tomography (PET) imaging with Prostate-Specific Membrane Antigen (PSMA) and Fluorodeoxyglucose (FDG) represent promising biomarkers for risk-stratification of Prostate Cancer (PCa). We verified whether the expression of genes encoding for PSMA and enzymes regulating FDG cellular uptake are independent and additive prognosticators in PCa.<h4>Methods</h4>mRNA expression of genes involved in glucose metabolism and PSMA regulation obtained from primary PCa specimens were retrieved from open-source databases and analyzed using an integrative bioinformatics approach. Machine Learning (ML) techniques were used to create predictive Progression-Free Survival (PFS) models. Cellular models of primary PCa with different aggressiveness were used to compare [18F]F-PS"],"journal":["Journal of translational medicine"],"pubmed_title":["Gene's expression underpinning the divergent predictive value of [18F]F-fluorodeoxyglucose and prostate-specific membrane antigen positron emission tomography in primary prostate cancer: a bioinformatic and experimental study."],"pmcid":["PMC9811737"],"funding_grant_id":["IG 23201"],"pubmed_authors":["Morbelli S","Chiola S","Piana M","Riondato M","Barboro P","Miceli A","Cossu V","Chiesa S","D'Amico F","Fornarini G","Sambuceti G","Vitale F","Carta S","Bruno S","Terrone C","Marini C","Orengo AM","Campi C","Signori A","Bauckneht M"],"additional_accession":[]},"is_claimable":false,"name":"Gene's expression underpinning the divergent predictive value of [18F]F-fluorodeoxyglucose and prostate-specific membrane antigen positron emission tomography in primary prostate cancer: a bioinformatic and experimental study.","description":"<h4>Background</h4>Positron Emission Tomography (PET) imaging with Prostate-Specific Membrane Antigen (PSMA) and Fluorodeoxyglucose (FDG) represent promising biomarkers for risk-stratification of Prostate Cancer (PCa). We verified whether the expression of genes encoding for PSMA and enzymes regulating FDG cellular uptake are independent and additive prognosticators in PCa.<h4>Methods</h4>mRNA expression of genes involved in glucose metabolism and PSMA regulation obtained from primary PCa specimens were retrieved from open-source databases and analyzed using an integrative bioinformatics approach. Machine Learning (ML) techniques were used to create predictive Progression-Free Survival (PFS) models. Cellular models of primary PCa with different aggressiveness were used to compare [18F]F-PS","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Jan","modification":"2025-04-19T09:21:53.495Z","creation":"2025-02-19T03:31:28.479Z"},"accession":"S-EPMC9811737","cross_references":{"pubmed":["36600265"],"doi":["10.1186/s12967-022-03846-1"]}}